Successor Features for Transfer in Reinforcement Learning
proceedings.neurips.cc · 7,434 words · saved by 1 readers
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Successor Features for Transfer in Reinforcement Learning André Barreto, Will Dabney, Rémi Munos, Jonathan J. Hunt, Tom Schaul, Hado van Hasselt, David Silver {andrebarreto,wdabney,munos,jjhunt,schaul,hado,davidsilver}@google.com DeepMind Abstract Transfer in reinforcement learning refers to the notion that generalization should occur not only within a task but also across tasks. We propose a transfer frame-…
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